{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c29c97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd435cfe",
   "metadata": {},
   "source": [
    "# 使用类的__init__进行实例化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0265fa76",
   "metadata": {},
   "source": [
    "messages: Sequence[MessageLikeRepresentation]\n",
    "```python\n",
    "class ChatPromptTemplate(BaseChatPromptTemplate):\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        messages: Sequence[MessageLikeRepresentation],\n",
    "        *,\n",
    "        template_format: PromptTemplateFormat = \"f-string\",\n",
    "        **kwargs: Any,\n",
    "    ) -> None:\n",
    "\n",
    "```\n",
    "messages的类型\n",
    "\n",
    "```python\n",
    "MessageLikeRepresentation = (\n",
    "    MessageLike\n",
    "    | tuple[str | type, str | list[dict] | list[object]]\n",
    "    | str\n",
    "    | dict[str, Any]\n",
    ")\n",
    "```\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "58f01eee",
   "metadata": {},
   "source": [
    "查看docstring\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56d4036e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "# 单变量\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"You are a helpful AI bot. Your name is Carl.\"),\n",
    "        (\"human\", \"{user_input}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "prompt_value = template.invoke(\"Hello, there!\")\n",
    "# Equivalent to\n",
    "# prompt_value = template.invoke({\"user_input\": \"Hello, there!\"})\n",
    "\n",
    "# Output:\n",
    "#  ChatPromptValue(\n",
    "#     messages=[\n",
    "#         SystemMessage(content='You are a helpful AI bot. Your name is Carl.'),\n",
    "#         HumanMessage(content='Hello, there!'),\n",
    "#     ]\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e1f1299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptValue(messages=[SystemMessage(content='你是文案生成器，把用户输入的内容改成活泼的小红书标题风格', additional_kwargs={}, response_metadata={}), HumanMessage(content='在家做奶茶', additional_kwargs={}, response_metadata={})])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "# 模板：只有一个变量{content}，用于快速生成指定类型文本\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是文案生成器，把用户输入的内容改成活泼的小红书标题风格\"),\n",
    "        (\"human\", \"{content}\"),\n",
    "    ]\n",
    ")\n",
    "# 简化调用：直接传字符串（等价于 {\"content\": \"在家做奶茶\"}）\n",
    "prompt_value = template.invoke(\"在家做奶茶\")\n",
    "prompt_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80a5357c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptValue(messages=[SystemMessage(content='You are a helpful AI bot. Your name is Bob.', additional_kwargs={}, response_metadata={}), HumanMessage(content='Hello, how are you doing?', additional_kwargs={}, response_metadata={}), AIMessage(content=\"I'm doing well, thanks!\", additional_kwargs={}, response_metadata={}), HumanMessage(content='What is your name?', additional_kwargs={}, response_metadata={})])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 官方docstring给的例子\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"You are a helpful AI bot. Your name is {name}.\"),\n",
    "        (\"human\", \"Hello, how are you doing?\"),\n",
    "        (\"ai\", \"I'm doing well, thanks!\"),\n",
    "        (\"human\", \"{user_input}\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "prompt_value = template.invoke(\n",
    "    {\n",
    "        \"name\": \"Bob\",\n",
    "        \"user_input\": \"What is your name?\",\n",
    "    }\n",
    ")\n",
    "prompt_value\n",
    "# Output:\n",
    "# ChatPromptValue(\n",
    "#    messages=[\n",
    "#        SystemMessage(content='You are a helpful AI bot. Your name is Bob.'),\n",
    "#        HumanMessage(content='Hello, how are you doing?'),\n",
    "#        AIMessage(content=\"I'm doing well, thanks!\"),\n",
    "#        HumanMessage(content='What is your name?')\n",
    "#    ]\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9cdb1847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptValue(messages=[SystemMessage(content='你是一个专业的数学老师，回答要简洁易懂，适合初中生。', additional_kwargs={}, response_metadata={}), HumanMessage(content='我刚学数学，能先讲个最基础的概念吗？', additional_kwargs={}, response_metadata={}), AIMessage(content='没问题！数学的核心基础是：用简单方法解决实际问题～', additional_kwargs={}, response_metadata={}), HumanMessage(content='那负数的加减法具体怎么理解呀？', additional_kwargs={}, response_metadata={})])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 举个中文的例子\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是一个专业的{subject}老师，回答要简洁易懂，适合初中生。\"),\n",
    "        (\"human\", \"我刚学{subject}，能先讲个最基础的概念吗？\"),\n",
    "        (\"ai\", \"没问题！{subject}的核心基础是：用简单方法解决实际问题～\"),\n",
    "        (\"human\", \"那{knowledge_point}具体怎么理解呀？\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 传入变量生成最终对话\n",
    "prompt_value = template.invoke(\n",
    "    {\n",
    "        \"subject\": \"数学\",\n",
    "        \"knowledge_point\": \"负数的加减法\"\n",
    "    }\n",
    ")\n",
    "prompt_value\n",
    "# ChatPromptValue(\n",
    "#     messages=[\n",
    "#         SystemMessage(content='你是一个专业的数学老师，回答要简洁易懂，适合初中生。', additional_kwargs={}, response_metadata={}),\n",
    "#         HumanMessage(content='我刚学数学，能先讲个最基础的概念吗？', additional_kwargs={}, response_metadata={}), \n",
    "#         AIMessage(content='没问题！数学的核心基础是：用简单方法解决实际问题～', additional_kwargs={}, response_metadata={}), \n",
    "#         HumanMessage(content='那负数的加减法具体怎么理解呀？', additional_kwargs={}, response_metadata={})\n",
    "#     ]\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e584e6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试1 - 不传入可选变量：\n",
      "SystemMessage: 你是贴心助手，回复不超过3句话。\n",
      "HumanMessage: 我想了解人工智能，能简单说说吗？\n",
      "\n",
      "测试2 - 传入可选变量：\n",
      "SystemMessage: 你是贴心助手，回复不超过3句话。\n",
      "HumanMessage: AI能做什么？\n",
      "AIMessage: AI可以聊天、画画、数据分析哦～\n",
      "HumanMessage: 我想了解人工智能，能简单说说吗？\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
    "# 能不能先空着，先用东西占个位，然后后面再填充 placeholder\n",
    "# 模板包含「可选的聊天历史占位符」（optional=True）\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是贴心助手，回复不超过3句话。\"),\n",
    "        # 可选占位符：用户可传可不传 chat_history，默认空列表\n",
    "        MessagesPlaceholder(variable_name=\"chat_history\", optional=True),\n",
    "        (\"human\", \"我想了解{topic}，能简单说说吗？\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 测试1：不传入可选变量 chat_history（用默认空列表）\n",
    "prompt1 = template.invoke({\"topic\": \"人工智能\"})\n",
    "print(\"测试1 - 不传入可选变量：\")\n",
    "for msg in prompt1.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")\n",
    "\n",
    "# 测试2：传入可选变量 chat_history（带历史对话）\n",
    "prompt2 = template.invoke(\n",
    "    {\n",
    "        \"topic\": \"人工智能\",\n",
    "        \"chat_history\": [\n",
    "            (\"human\", \"AI能做什么？\"),\n",
    "            (\"ai\", \"AI可以聊天、画画、数据分析哦～\"),\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "print(\"\\n测试2 - 传入可选变量：\")\n",
    "for msg in prompt2.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "063cbb0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试3 - 部分变量预先填充：\n",
      "SystemMessage: 你是科普小达人，专业解答自然科学问题。\n",
      "HumanMessage: 请问为什么树叶秋天会变黄？？\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "# 模板包含固定角色名（通过 partial_variables 预先填充，无需用户传入）\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是{bot_name}，专业解答{field}问题。\"), # 系统提示词\n",
    "        (\"human\", \"请问{question}？\"), # 用户说的话\n",
    "    ],\n",
    "    partial_variables={\"bot_name\": \"科普小达人\", \"field\": \"自然科学\"}  # 预先填充的部分变量\n",
    ")\n",
    "\n",
    "# 测试：只需传入必填变量 question\n",
    "prompt = template.invoke({\"question\": \"为什么树叶秋天会变黄？\"})\n",
    "print(\"测试3 - 部分变量预先填充：\")\n",
    "for msg in prompt.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "74022293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptValue(messages=[SystemMessage(content='You are a helpful AI bot.', additional_kwargs={}, response_metadata={}), HumanMessage(content='Hi!', additional_kwargs={}, response_metadata={}), AIMessage(content='How can I assist you today?', additional_kwargs={}, response_metadata={}), HumanMessage(content='Can you make me an ice cream sundae?', additional_kwargs={}, response_metadata={}), AIMessage(content='No.', additional_kwargs={}, response_metadata={})])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"You are a helpful AI bot.\"), # 对应SystemMessage系统提示词\n",
    "        # Means the template will receive an optional list of messages under\n",
    "        # the \"conversation\" key\n",
    "        (\"placeholder\", \"{conversation}\"),\n",
    "        # Equivalently:\n",
    "        # MessagesPlaceholder(variable_name=\"conversation\", optional=True)\n",
    "    ]\n",
    ")\n",
    "# 这里把conversation填充好\n",
    "# placeholder 这个词语，后面视觉大模型训练的时候，图片占位也会用到这个词语\n",
    "prompt_value = template.invoke(\n",
    "    {\n",
    "        \"conversation\": [\n",
    "            (\"human\", \"Hi!\"),\n",
    "            (\"ai\", \"How can I assist you today?\"),\n",
    "            (\"human\", \"Can you make me an ice cream sundae?\"),\n",
    "            (\"ai\", \"No.\"),\n",
    "        ]\n",
    "    }\n",
    ")\n",
    "prompt_value\n",
    "# ChatPromptValue(\n",
    "#    messages=[\n",
    "#        SystemMessage(content='You are a helpful AI bot.'),\n",
    "#        HumanMessage(content='Hi!'),\n",
    "#        AIMessage(content='How can I assist you today?'),\n",
    "#        HumanMessage(content='Can you make me an ice cream sundae?'),\n",
    "#        AIMessage(content='No.'),\n",
    "#    ]\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f62c9452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatPromptValue(messages=[SystemMessage(content='你是贴心的购物助手，记住用户之前提到的需求，不要重复提问。', additional_kwargs={}, response_metadata={}), HumanMessage(content='我想买一款适合学生党的平板电脑，预算3000元内', additional_kwargs={}, response_metadata={}), AIMessage(content='好的～ 学生党平板重点看学习功能和续航，我记下来啦！', additional_kwargs={}, response_metadata={}), HumanMessage(content='对了，还要支持手写笔，用来记笔记', additional_kwargs={}, response_metadata={}), HumanMessage(content='基于我之前说的，帮我推荐最合适的平板电脑吧！', additional_kwargs={}, response_metadata={})])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "# 模板：系统角色+历史对话占位符+当前用户提问\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是贴心的购物助手，记住用户之前提到的需求，不要重复提问。\"),\n",
    "        (\"placeholder\", \"{chat_history}\"),  # 接收历史对话列表\n",
    "        (\"human\", \"基于我之前说的，帮我推荐最合适的{product_type}吧！\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 模拟历史对话（实际项目中可从数据库读取）\n",
    "history = [\n",
    "    (\"human\", \"我想买一款适合学生党的平板电脑，预算3000元内\"),\n",
    "    (\"ai\", \"好的～ 学生党平板重点看学习功能和续航，我记下来啦！\"),\n",
    "    (\"human\", \"对了，还要支持手写笔，用来记笔记\"),\n",
    "]\n",
    "\n",
    "# 生成带历史的提示词\n",
    "prompt_value = template.invoke(\n",
    "    {\n",
    "        \"chat_history\": history,\n",
    "        \"product_type\": \"平板电脑\"\n",
    "    }\n",
    ")\n",
    "\n",
    "# 输出结果（简化展示）：\n",
    "# ChatPromptValue(\n",
    "# messages=[SystemMessage(content='你是贴心的购物助手，记住用户之前提到的需求，不要重复提问。', additional_kwargs={}, response_metadata={}), \n",
    "# HumanMessage(content='我想买一款适合学生党的平板电脑，预算3000元内', additional_kwargs={}, response_metadata={}), \n",
    "# AIMessage(content='好的～ 学生党平板重点看学习功能和续航，我记下来啦！', additional_kwargs={}, response_metadata={}), \n",
    "# HumanMessage(content='对了，还要支持手写笔，用来记笔记', additional_kwargs={}, response_metadata={}), \n",
    "# HumanMessage(content='基于我之前说的，帮我推荐最合适的平板电脑吧！', additional_kwargs={}, response_metadata={})])\n",
    "prompt_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b51d6f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "# 模板：系统角色+历史对话占位符+当前用户提问\n",
    "template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是贴心的购物助手，记住用户之前提到的需求，不要重复提问。\"),\n",
    "        (\"placeholder\", \"{chat_history}\"),  # 接收历史对话列表\n",
    "        (\"human\", \"基于我之前说的，帮我推荐最合适的{product_type}吧！\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 通过placeholder进行占位，后进行填充\n",
    "# 模拟历史对话（实际项目中可从数据库读取）\n",
    "history = [\n",
    "    (\"human\", \"我想买一款适合学生党的平板电脑，预算3000元内\"),\n",
    "    (\"ai\", \"好的～ 学生党平板重点看学习功能和续航，我记下来啦！\"),\n",
    "    (\"human\", \"对了，还要支持手写笔，用来记笔记\"),\n",
    "]\n",
    "\n",
    "# 生成带历史的提示词\n",
    "prompt_value = template.invoke(\n",
    "    {\n",
    "        \"chat_history\": history, # 这里\"chat_history\" 不能写成其他的\n",
    "        \"product_type\": \"平板电脑\"\n",
    "    }\n",
    ")\n",
    "\n",
    "# 输出结果（简化展示）：\n",
    "# [\n",
    "#   SystemMessage(content='你是贴心的购物助手，记住用户之前提到的需求，不要重复提问。'),\n",
    "#   HumanMessage(content='我想买一款适合学生党的平板电脑，预算3000元内'),\n",
    "#   AIMessage(content='好的～ 学生党平板重点看学习功能和续航，我记下来啦！'),\n",
    "#   HumanMessage(content='对了，还要支持手写笔，用来记笔记'),\n",
    "#   HumanMessage(content='基于我之前说的，帮我推荐最合适的平板电脑吧！')\n",
    "# ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5387371",
   "metadata": {},
   "source": [
    "# 使用from_messages进行实例化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28f78517",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 也可以用from_messages\n",
    "# 源码from_messages中参数messages\n",
    "# MessageLikeRepresentation = (\n",
    "#     MessageLike\n",
    "#     | tuple[str | type, str | list[dict] | list[object]]\n",
    "#     | str\n",
    "#     | dict[str, Any]\n",
    "# )\n",
    "# 下面举例是元组\n",
    "template = ChatPromptTemplate.from_messages([\n",
    "    (\"system\", \"你是{role}，需要回答关于{topic}的问题。\"),  # 含变量的system消息\n",
    "    (\"human\", \"请解释一下{concept}是什么？\"),               # 含变量的human消息\n",
    "])\n",
    "\n",
    "# 传入变量生成消息\n",
    "messages = template.format_messages(\n",
    "    role=\"物理老师\",\n",
    "    topic=\"力学\",\n",
    "    concept=\"牛顿第三定律\"\n",
    ")\n",
    "\n",
    "# messages: 消息表示形式的序列。\n",
    "#     消息可通过以下格式表示：\n",
    "#     1. `BaseMessagePromptTemplate` 类实例\n",
    "#     2. `BaseMessage` 类实例\n",
    "#     3. 二元元组 `(消息类型, 模板)`；例如 `(\"human\", \"{user_input}\")`\n",
    "#     4. 二元元组 `(消息类, 模板)`\n",
    "#     5. 字符串（简写形式，等价于 `(\"human\", 模板)`）；例如 `\"{user_input}\"`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a623779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SystemMessage(content='你是物理老师，需要回答关于力学的问题。', additional_kwargs={}, response_metadata={}), HumanMessage(content='请解释一下牛顿第三定律是什么？', additional_kwargs={}, response_metadata={})]\n"
     ]
    }
   ],
   "source": [
    "template = ChatPromptTemplate.from_messages([\n",
    "    (\"system\", \"你是{role}，需要回答关于{topic}的问题。\"),  # 含变量的system消息\n",
    "    (\"human\", \"请解释一下{concept}是什么？\"),               # 含变量的human消息\n",
    "])\n",
    "\n",
    "# 传入变量生成消息\n",
    "messages = template.format_messages(\n",
    "    role=\"物理老师\",\n",
    "    topic=\"力学\",\n",
    "    concept=\"牛顿第三定律\"\n",
    ")\n",
    "print(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5770a6ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SystemMessage(content='你是一个营养师，根据用户信息提供饮食建议。', additional_kwargs={}, response_metadata={}), HumanMessage(content=[{'type': 'text', 'text': '我的情况如下：'}, {'type': 'text', 'text': '- 年龄：35'}, {'type': 'text', 'text': '- 需求：减脂期间的早餐搭配'}, {'type': 'text', 'text': '请给出具体建议。'}], additional_kwargs={}, response_metadata={})]\n"
     ]
    }
   ],
   "source": [
    "# 我们举dict的例子\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "template = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        {\"role\": \"system\", \"content\": \"你是一个营养师，根据用户信息提供饮食建议。\"},\n",
    "        {\"role\": \"human\", \"content\": [\n",
    "            \"我的情况如下：\",\n",
    "            \"- 年龄：{age}\",\n",
    "            \"- 需求：{need}\",\n",
    "            \"请给出具体建议。\"\n",
    "            ]\n",
    "        },\n",
    "    ]\n",
    ")\n",
    "\n",
    "messages = template.format_messages(\n",
    "    age=35,\n",
    "    need=\"减脂期间的早餐搭配\"\n",
    ")\n",
    "print(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "38cc9fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SystemMessage(content='你是一个产品经理，需精准提炼用户需求的核心痛点和改进方向。', additional_kwargs={}, response_metadata={}), HumanMessage(content=[{'type': 'text', 'text': '请分析以下用户反馈的需求点：'}, {'type': 'text'}], additional_kwargs={}, response_metadata={})]\n"
     ]
    }
   ],
   "source": [
    "# 我们举dict的例子，自己多编编即可，熟悉熟悉\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "\n",
    "# 定义模板：system指定角色，human包含固定引导语+文本类型的用户输入\n",
    "prompt = ChatPromptTemplate.from_messages([\n",
    "    {\"role\": \"system\", \"content\": \"你是一个{role}，需精准提炼用户需求的核心痛点和改进方向。\"},\n",
    "    {\"role\": \"human\", \"content\": [\"请分析以下用户反馈的需求点：\", {\"type\": \"text\"}]}\n",
    "])\n",
    "\n",
    "# 注入角色并生成消息\n",
    "messages = prompt.format_messages(\n",
    "    role=\"产品经理\",\n",
    "    text=\"这个APP的搜索功能太麻烦了，每次都要翻好几页才能找到结果，能不能加个筛选条件？\"\n",
    ")\n",
    "print(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1fc005de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'System: 你是科普小达人，专业解答自然科学问题。\\nHuman: 请问question？'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用format进行填充\n",
    "prompt_template = template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是{bot_name}，专业解答{field}问题。\"),\n",
    "        (\"human\", \"请问{question}？\"),\n",
    "    ],\n",
    ")\n",
    "prompt_value = prompt_template.format(bot_name=\"科普小达人\", field=\"自然科学\", question=\"question\")\n",
    "# 这里是字符串\n",
    "prompt_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5300282b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prompt_value:  messages=[SystemMessage(content='你是科普小达人，专业解答自然科学问题。', additional_kwargs={}, response_metadata={}), HumanMessage(content='请问question？', additional_kwargs={}, response_metadata={})]\n",
      "prompt_value.to_messages:  <bound method ChatPromptValue.to_messages of ChatPromptValue(messages=[SystemMessage(content='你是科普小达人，专业解答自然科学问题。', additional_kwargs={}, response_metadata={}), HumanMessage(content='请问question？', additional_kwargs={}, response_metadata={})])>\n"
     ]
    }
   ],
   "source": [
    "# 使用format_prompt\n",
    "prompt_template = template = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是{bot_name}，专业解答{field}问题。\"),\n",
    "        (\"human\", \"请问{question}？\"),\n",
    "    ],\n",
    ")\n",
    "prompt_value = prompt_template.format_prompt(bot_name=\"科普小达人\", field=\"自然科学\", question=\"question\")\n",
    "print(\"prompt_value: \", prompt_value)\n",
    "print(\"prompt_value.to_messages: \", prompt_value.to_messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9abf0f0",
   "metadata": {},
   "source": [
    "# + 加号重载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "70fe68b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# 对加号的重载\\ndef __add__(self, other: Any) -> ChatPromptTemplate:\\n    # 1. 复制当前模板的「部分变量」，后续合并另一对象的部分变量\\n    partials = {**self.partial_variables}\\n\\n    # 2. 检查被合并的对象（other）是否有部分变量，若有则合并到 partials 中\\n    # （兼容非 ChatPromptTemplate 类型的对象，避免属性不存在报错）\\n    if hasattr(other, \"partial_variables\") and other.partial_variables:\\n        partials.update(other.partial_variables)\\n\\n    # 3. 分支1：other 是 ChatPromptTemplate 实例（直接拼接消息列表）\\n    if isinstance(other, ChatPromptTemplate):\\n        return ChatPromptTemplate(messages=self.messages + other.messages).partial(\\n            **partials  # 合并后的部分变量传入\\n        )\\n    # 4. 分支2：other 是单个消息模板/消息实例（直接加入当前消息列表）\\n    if isinstance(\\n        other, (BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate)\\n    ):\\n        return ChatPromptTemplate(messages=[*self.messages, other]).partial(\\n            **partials\\n        )\\n    # 5. 分支3：other 是列表/元组（先转成 ChatPromptTemplate，再拼接消息）\\n    if isinstance(other, (list, tuple)):\\n        other_ = ChatPromptTemplate.from_messages(other)  # 列表转模板\\n        return ChatPromptTemplate(messages=self.messages + other_.messages).partial(\\n            **partials\\n        )\\n    # 6. 分支4：other 是字符串（默认转成 HumanMessage 模板，再加入）\\n    if isinstance(other, str):\\n        prompt = HumanMessagePromptTemplate.from_template(other)  # 字符串转人类消息模板\\n        return ChatPromptTemplate(messages=[*self.messages, prompt]).partial(\\n            **partials\\n        )\\n    # 7. 不支持的类型：抛出异常\\n    msg = f\"不支持的合并类型：{type(other)}（仅支持 ChatPromptTemplate、消息、列表、元组、字符串）\"\\n    raise NotImplementedError(msg)\\n\\n'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "# 对加号的重载\n",
    "def __add__(self, other: Any) -> ChatPromptTemplate:\n",
    "    # 1. 复制当前模板的「部分变量」，后续合并另一对象的部分变量\n",
    "    partials = {**self.partial_variables}\n",
    "\n",
    "    # 2. 检查被合并的对象（other）是否有部分变量，若有则合并到 partials 中\n",
    "    # （兼容非 ChatPromptTemplate 类型的对象，避免属性不存在报错）\n",
    "    if hasattr(other, \"partial_variables\") and other.partial_variables:\n",
    "        partials.update(other.partial_variables)\n",
    "\n",
    "    # 3. 分支1：other 是 ChatPromptTemplate 实例（直接拼接消息列表）\n",
    "    if isinstance(other, ChatPromptTemplate):\n",
    "        return ChatPromptTemplate(messages=self.messages + other.messages).partial(\n",
    "            **partials  # 合并后的部分变量传入\n",
    "        )\n",
    "    # 4. 分支2：other 是单个消息模板/消息实例（直接加入当前消息列表）\n",
    "    if isinstance(\n",
    "        other, (BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate)\n",
    "    ):\n",
    "        return ChatPromptTemplate(messages=[*self.messages, other]).partial(\n",
    "            **partials\n",
    "        )\n",
    "    # 5. 分支3：other 是列表/元组（先转成 ChatPromptTemplate，再拼接消息）\n",
    "    if isinstance(other, (list, tuple)):\n",
    "        other_ = ChatPromptTemplate.from_messages(other)  # 列表转模板\n",
    "        return ChatPromptTemplate(messages=self.messages + other_.messages).partial(\n",
    "            **partials\n",
    "        )\n",
    "    # 6. 分支4：other 是字符串（默认转成 HumanMessage 模板，再加入）\n",
    "    if isinstance(other, str):\n",
    "        prompt = HumanMessagePromptTemplate.from_template(other)  # 字符串转人类消息模板\n",
    "        return ChatPromptTemplate(messages=[*self.messages, prompt]).partial(\n",
    "            **partials\n",
    "        )\n",
    "    # 7. 不支持的类型：抛出异常\n",
    "    msg = f\"不支持的合并类型：{type(other)}（仅支持 ChatPromptTemplate、消息、列表、元组、字符串）\"\n",
    "    raise NotImplementedError(msg)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "694ef584",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 测试1：ChatPromptTemplate + ChatPromptTemplate ===\n",
      "合并后的消息模板列表：\n",
      "  消息1：prompt=PromptTemplate(input_variables=['bot_name'], input_types={}, partial_variables={}, template='你是{bot_name}，回答简洁。') additional_kwargs={}\n",
      "  消息2：prompt=PromptTemplate(input_variables=['topic'], input_types={}, partial_variables={}, template='我想了解{topic}的基础。') additional_kwargs={}\n",
      "  消息3：prompt=PromptTemplate(input_variables=['topic'], input_types={}, partial_variables={}, template='好的！{topic}的核心是解决实际问题～') additional_kwargs={}\n",
      "  消息4：prompt=PromptTemplate(input_variables=['detail_question'], input_types={}, partial_variables={}, template='{detail_question}') additional_kwargs={}\n",
      "合并后的部分变量： {'bot_name': '技术顾问', 'topic': 'Python编程'}\n",
      "\n",
      "最终生成的提示词消息：\n",
      "SystemMessage: 你是技术顾问，回答简洁。\n",
      "HumanMessage: 我想了解Python编程的基础。\n",
      "AIMessage: 好的！Python编程的核心是解决实际问题～\n",
      "HumanMessage: 怎么快速入门？\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.messages import AIMessage\n",
    "# 测试例 1：ChatPromptTemplate + ChatPromptTemplate\n",
    "# 模板1：系统角色 + 部分变量（固定 bot_name）\n",
    "template1 = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"system\", \"你是{bot_name}，回答简洁。\"), \n",
    "        (\"human\", \"我想了解{topic}的基础。\")\n",
    "    ],\n",
    "    partial_variables={\"bot_name\": \"技术顾问\"}  # 部分变量\n",
    ")\n",
    "\n",
    "# 模板2：AI 回复 + 人类追问（含部分变量）\n",
    "template2 = ChatPromptTemplate(\n",
    "    [\n",
    "        (\"ai\", \"好的！{topic}的核心是解决实际问题～\"), \n",
    "        (\"human\", \"{detail_question}\")\n",
    "    ],\n",
    "    partial_variables={\"topic\": \"Python编程\"}  # 部分变量（覆盖template1的topic）\n",
    ")\n",
    "\n",
    "# 合并模板（用 + 运算符）\n",
    "combined_template = template1 + template2\n",
    "\n",
    "# Debug 验证：打印合并后的消息模板和变量（避免直接访问content）\n",
    "print(\"=== 测试1：ChatPromptTemplate + ChatPromptTemplate ===\")\n",
    "print(\"合并后的消息模板列表：\")\n",
    "for i, msg_template in enumerate(combined_template.messages):\n",
    "    # 打印模板的角色和内容模板（而非content，因为是模板对象）\n",
    "    print(f\"  消息{i+1}：{msg_template}\")\n",
    "print(\"合并后的部分变量：\", combined_template.partial_variables)  # 合并后的变量（topic被template2覆盖）\n",
    "\n",
    "# 调用合并后的模板（传未被部分变量填充的必填项：topic在template2中已固定，这里只需传detail_question）\n",
    "# 注意：template1中的{topic}会被template2的partial_variables覆盖，最终topic为\"Python编程\"\n",
    "prompt = combined_template.invoke({\"detail_question\": \"怎么快速入门？\"})\n",
    "print(\"\\n最终生成的提示词消息：\")\n",
    "for msg in prompt.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "77d3ab2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 测试2：ChatPromptTemplate + 单个消息实例 ===\n",
      "合并后的消息列表：\n",
      "  消息1：prompt=PromptTemplate(input_variables=['type'], input_types={}, partial_variables={}, template='推荐一本{type}书。') additional_kwargs={}\n",
      "  消息2：content='推荐时必须包含书名和核心亮点，不超过20字' additional_kwargs={} response_metadata={}\n",
      "\n",
      "最终提示词：\n",
      "HumanMessage: 推荐一本科幻书。\n",
      "SystemMessage: 推荐时必须包含书名和核心亮点，不超过20字\n"
     ]
    }
   ],
   "source": [
    "# 测试例 2：ChatPromptTemplate + 单个消息实例\n",
    "# 基础模板\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.messages import SystemMessage\n",
    "base_template = ChatPromptTemplate([(\"human\", \"推荐一本{type}书。\")])\n",
    "\n",
    "# 追加单个 SystemMessage（限制回复风格）\n",
    "added_msg = SystemMessage(content=\"推荐时必须包含书名和核心亮点，不超过20字\")\n",
    "\n",
    "# 合并\n",
    "combined_template = base_template + added_msg\n",
    "\n",
    "# Debug 验证\n",
    "print(\"\\n=== 测试2：ChatPromptTemplate + 单个消息实例 ===\")\n",
    "print(\"合并后的消息列表：\")\n",
    "for i, msg in enumerate(combined_template.messages):\n",
    "    print(f\"  消息{i+1}：{msg}\")\n",
    "\n",
    "# 调用\n",
    "prompt = combined_template.invoke({\"type\": \"科幻\"})\n",
    "print(\"\\n最终提示词：\")\n",
    "for msg in prompt.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3392fbf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 测试3：ChatPromptTemplate + 列表 ===\n",
      "合并后的消息列表：\n",
      "  消息1：prompt=PromptTemplate(input_variables=['city'], input_types={}, partial_variables={}, template='你是美食博主，推荐{city}美食。') additional_kwargs={}\n",
      "  消息2：prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='我爱吃辣，有没有推荐？') additional_kwargs={}\n",
      "  消息3：content='必须安排！成都辣美食yyds～' additional_kwargs={} response_metadata={}\n",
      "  消息4：prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='具体推荐3家店吧！') additional_kwargs={}\n",
      "\n",
      "最终提示词：\n",
      "SystemMessage: 你是美食博主，推荐成都美食。\n",
      "HumanMessage: 我爱吃辣，有没有推荐？\n",
      "AIMessage: 必须安排！成都辣美食yyds～\n",
      "HumanMessage: 具体推荐3家店吧！\n"
     ]
    }
   ],
   "source": [
    "# 测试例 3：ChatPromptTemplate + 列表/元组\n",
    "# 基础模板\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.messages import AIMessage\n",
    "base_template = ChatPromptTemplate([(\"system\", \"你是美食博主，推荐{city}美食。\")])\n",
    "\n",
    "# 要追加的消息列表（混合元组和消息实例）\n",
    "added_messages = [\n",
    "    (\"human\", \"我爱吃辣，有没有推荐？\"),\n",
    "    AIMessage(content=\"必须安排！成都辣美食yyds～\"),\n",
    "    (\"human\", \"具体推荐3家店吧！\")\n",
    "]\n",
    "\n",
    "# 合并（支持列表或元组，这里用列表）\n",
    "combined_template = base_template + added_messages\n",
    "\n",
    "# Debug 验证\n",
    "print(\"\\n=== 测试3：ChatPromptTemplate + 列表 ===\")\n",
    "print(\"合并后的消息列表：\")\n",
    "for i, msg in enumerate(combined_template.messages):\n",
    "    print(f\"  消息{i+1}：{msg}\")\n",
    "\n",
    "# 调用\n",
    "prompt = combined_template.invoke({\"city\": \"成都\"})\n",
    "print(\"\\n最终提示词：\")\n",
    "for msg in prompt.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46102f62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 测试4：ChatPromptTemplate + 字符串 ===\n",
      "合并后的消息列表：\n",
      "  消息1：prompt=PromptTemplate(input_variables=['user_type'], input_types={}, partial_variables={}, template='你是旅行顾问，回答{user_type}的旅行问题。') additional_kwargs={}\n",
      "  消息2：prompt=PromptTemplate(input_variables=['place'], input_types={}, partial_variables={}, template='我要去{place}旅行，需要注意什么？') additional_kwargs={}\n",
      "  消息3：prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='另外，推荐2个小众景点吧！') additional_kwargs={}\n",
      "\n",
      "最终提示词：\n",
      "SystemMessage: 你是旅行顾问，回答学生的旅行问题。\n",
      "HumanMessage: 我要去云南旅行，需要注意什么？\n",
      "HumanMessage: 另外，推荐2个小众景点吧！\n"
     ]
    }
   ],
   "source": [
    "# 测试例 4：ChatPromptTemplate + 字符串\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "# 基础模板\n",
    "base_template = ChatPromptTemplate([\n",
    "    (\"system\", \"你是旅行顾问，回答{user_type}的旅行问题。\"),\n",
    "    (\"human\", \"我要去{place}旅行，需要注意什么？\")\n",
    "])\n",
    "\n",
    "# 追加字符串（默认转 HumanMessage）\n",
    "added_str = \"另外，推荐2个小众景点吧！\"\n",
    "combined_template = base_template + added_str\n",
    "\n",
    "# Debug 验证\n",
    "print(\"\\n=== 测试4：ChatPromptTemplate + 字符串 ===\")\n",
    "print(\"合并后的消息列表：\")\n",
    "for i, msg in enumerate(combined_template.messages):\n",
    "    print(f\"  消息{i+1}：{msg}\")\n",
    "\n",
    "# 调用\n",
    "prompt = combined_template.invoke({\"user_type\": \"学生\", \"place\": \"云南\"})\n",
    "print(\"\\n最终提示词：\")\n",
    "for msg in prompt.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5daefe64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 示例5：混合类型合并 ===\n",
      "合并后的消息列表：\n",
      "  1. prompt=PromptTemplate(input_variables=['job', 'problem_type'], input_types={}, partial_variables={}, template='你是{job}，解决{problem_type}问题') additional_kwargs={}\n",
      "  2. prompt=PromptTemplate(input_variables=['specific_problem'], input_types={}, partial_variables={}, template='我遇到了{specific_problem}') additional_kwargs={}\n",
      "  3. content='已收到，正在处理...' additional_kwargs={} response_metadata={}\n",
      "  4. prompt=PromptTemplate(input_variables=['num'], input_types={}, partial_variables={}, template='请给出{num}个解决方案！') additional_kwargs={}\n",
      "必填变量： ['job', 'num', 'problem_type', 'specific_problem']\n",
      "SystemMessage: 你是程序员，解决代码报错问题\n",
      "HumanMessage: 我遇到了Python导入模块失败\n",
      "AIMessage: 已收到，正在处理...\n",
      "HumanMessage: 请给出2个解决方案！\n"
     ]
    }
   ],
   "source": [
    "# 混合混合\n",
    "# 基础模板\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "from langchain_core.messages import AIMessage\n",
    "base = ChatPromptTemplate([(\"system\", \"你是{job}，解决{problem_type}问题\")])\n",
    "\n",
    "# 要追加的混合对象：列表（含元组+消息实例） + 字符串\n",
    "append_list = [\n",
    "    (\"human\", \"我遇到了{specific_problem}\"),\n",
    "    AIMessage(content=\"已收到，正在处理...\")\n",
    "]\n",
    "append_str = \"请给出{num}个解决方案！\"\n",
    "\n",
    "# 链式合并：base + 列表 + 字符串\n",
    "combined = base + append_list + append_str\n",
    "\n",
    "# Debug 关键：验证所有对象都被正确转换\n",
    "print(\"\\n=== 示例5：混合类型合并 ===\")\n",
    "print(\"合并后的消息列表：\")\n",
    "for i, msg in enumerate(combined.messages):\n",
    "    print(f\"  {i+1}. {msg}\")\n",
    "print(\"必填变量：\", combined.input_variables)  # 输出：['job', 'problem_type', 'specific_problem', 'num']\n",
    "\n",
    "# 调用验证\n",
    "prompt = combined.invoke({\n",
    "    \"job\": \"程序员\",\n",
    "    \"problem_type\": \"代码报错\",\n",
    "    \"specific_problem\": \"Python导入模块失败\",\n",
    "    \"num\": 2\n",
    "})\n",
    "for msg in prompt.messages:\n",
    "    print(f\"{type(msg).__name__}: {msg.content}\")"
   ]
  }
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